table_1 = [[14.0, 632, 0.051115, 669, 0.056638, 701, 0.054567, 740, 0.04549, 780, 0.039706, 867, 0.043592, 919, 0.052356, 1054, 0.096974, 1112, 0.087499, 1213, 0.085689, 1311, 0.076069, 1392, 0.051421, 1448, 0.053964, 1565, 0.031643, 1610, 0.056224, 1720, 0.101885, 2859, 0.057873, 2919, 0.065195, 3282, 0.125981], [13.0, 630, 0.055598, 665, 0.056556, 698, 0.054116, 757, 0.053627, 780, 0.046325, 875, 0.047649, 944, 0.053033, 1033, 0.116856, 1085, 0.093651, 1211, 0.099136, 1322, 0.085076, 1392, 0.060874, 1446, 0.062876, 1540, 0.039503, 1610, 0.069761, 1716, 0.084561, 2856, 0.063997, 2919, 0.075906, 3286, 0.125071], [17.0, 634, 0.083325, 673, 0.09795, 703, 0.0961, 740, 0.075153, 794, 0.066265, 865, 0.070687, 919, 0.09051, 1058, 0.19569, 1112, 0.180612, 1234, 0.139464, 1305, 0.133142, 1415, 0.099761, 1448, 0.102653, 1565, 0.048946, 1614, 0.08505, 1722, 0.227247, 2873, 0.120497, 2919, 0.13479, 3286, 0.333413], [16.0, 634, 0.082214, 673, 0.086778, 705, 0.086148, 771, 0.076201, 780, 0.074736, 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table_2 = [[14.0, 4291, 0.728241, 4370, 0.716736, 4596, 0.71663, 4696, 0.721476, 5182, 0.655691, 6027, 0.481391, 6489, 0.493881, 6817, 0.519365], [13.0, 4291, 0.79156, 4395, 0.775894, 4646, 0.77548, 4692, 0.782304, 5178, 0.725942, 6027, 0.519638, 6489, 0.533919, 6821, 0.567838], [17.0, 4249, 0.793233, 4392, 0.777051, 4646, 0.7738, 4708, 0.790108, 5174, 0.711362, 6108, 0.499549, 6327, 0.534375, 6798, 0.549474], [16.0, 4326, 0.767614, 4388, 0.765803, 4646, 0.725515, 4727, 0.746978, 5167, 0.696696, 6108, 0.49159, 6489, 0.518258, 6852, 0.547933], [17.0, 4249, 0.79042, 4388, 0.774746, 4646, 0.769797, 4704, 0.784974, 5174, 0.706286, 6108, 0.502856, 6327, 0.536423, 6798, 0.551799], [5.0, 4295, 0.847801, 4395, 0.827524, 4646, 0.821708, 4700, 0.832943, 5178, 0.784482, 6027, 0.553516, 6489, 0.578306, 6813, 0.619906], [7.0, 4291, 0.73568, 4370, 0.72227, 4569, 0.721135, 4696, 0.724274, 5178, 0.666904, 6023, 0.487644, 6324, 0.502456, 6802, 0.51739], [1.0, 4291, 0.759536, 4370, 0.745434, 4646, 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4696, 0.742264, 5190, 0.698422, 6023, 0.49744, 6489, 0.510175, 6806, 0.535612], [4.0, 4245, 0.727017, 4370, 0.714098, 4596, 0.714646, 4704, 0.721155, 5171, 0.643655, 6027, 0.470058, 6320, 0.500726, 6786, 0.502471], [11.0, 4291, 0.73344, 4370, 0.721087, 4561, 0.711567, 4696, 0.712221, 5190, 0.666464, 6023, 0.493607, 6489, 0.505723, 6806, 0.528043], [13.0, 4291, 0.821301, 4392, 0.803342, 4646, 0.803971, 4692, 0.811532, 5178, 0.748755, 6027, 0.531482, 6489, 0.54697, 6817, 0.582418], [9.0, 4291, 0.800352, 4395, 0.783554, 4646, 0.78276, 4700, 0.790813, 5178, 0.741585, 6027, 0.519354, 6489, 0.538165, 6802, 0.565828], [15.0, 4249, 0.764737, 4370, 0.749476, 4611, 0.742757, 4704, 0.757465, 5178, 0.649881, 6023, 0.476023, 6320, 0.508509, 6790, 0.514078], [8.0, 4291, 0.773737, 4392, 0.759034, 4646, 0.752698, 4696, 0.759352, 5178, 0.704219, 6027, 0.506664, 6489, 0.519605, 6809, 0.546654], [15.0, 4245, 0.733248, 4370, 0.721404, 4565, 0.713214, 4704, 0.720177, 5178, 0.65475, 5955, 0.486046, 6320, 0.514255, 6798, 0.522203], [6.0, 4287, 0.770729, 4370, 0.755343, 4604, 0.749016, 4696, 0.754451, 5178, 0.68839, 6023, 0.501857, 6327, 0.513746, 6802, 0.534786], [1.0, 4291, 0.755944, 4370, 0.741897, 4646, 0.742196, 4692, 0.747571, 5178, 0.680593, 6027, 0.502233, 6489, 0.513306, 6813, 0.540948], [8.0, 4291, 0.76444, 4370, 0.748963, 4646, 0.745872, 4696, 0.752443, 5178, 0.691892, 6027, 0.497286, 6489, 0.509807, 6806, 0.536579], [14.0, 4291, 0.738462, 4370, 0.725901, 4596, 0.725683, 4696, 0.731193, 5182, 0.675231, 6023, 0.483273, 6489, 0.495627, 6817, 0.523228], [9.0, 4291, 0.801, 4392, 0.782108, 4646, 0.780666, 4700, 0.789719, 5178, 0.739554, 6023, 0.516786, 6489, 0.537092, 6806, 0.565323], [7.0, 4295, 0.746356, 4370, 0.734772, 4592, 0.729625, 4696, 0.734505, 5178, 0.680341, 6027, 0.497646, 6489, 0.512143, 6802, 0.531092], [9.0, 4291, 0.808201, 4392, 0.791917, 4646, 0.789342, 4700, 0.797503, 5178, 0.73595, 6027, 0.519279, 6370, 0.537962, 6802, 0.563523], [14.0, 4291, 0.742352, 4370, 0.729122, 4646, 0.730941, 4696, 0.737275, 5182, 0.677217, 6023, 0.479972, 6489, 0.492464, 6817, 0.520025], [9.0, 4287, 0.774426, 4395, 0.759668, 4646, 0.754607, 4700, 0.763183, 5178, 0.704013, 6027, 0.503018, 6489, 0.522754, 6806, 0.547746], [6.0, 4291, 0.753283, 4370, 0.738351, 4592, 0.732891, 4696, 0.737664, 5178, 0.676377, 6023, 0.490297, 6331, 0.50091, 6809, 0.52135], [17.0, 4291, 0.766355, 4392, 0.750168, 4646, 0.750366, 4692, 0.755562, 5190, 0.711029, 6027, 0.518396, 6489, 0.529282, 6817, 0.561487], [5.0, 4291, 0.844868, 4388, 0.824841, 4646, 0.819414, 4700, 0.828945, 5178, 0.777488, 6027, 0.543949, 6489, 0.564344, 6806, 0.603024], [17.0, 4249, 0.794775, 4392, 0.778801, 4646, 0.774537, 4704, 0.791032, 5174, 0.703313, 6108, 0.500122, 6324, 0.535558, 6798, 0.545699], [8.0, 4291, 0.763868, 4392, 0.75009, 4646, 0.74457, 4696, 0.750626, 5178, 0.699158, 6027, 0.503843, 6489, 0.517078, 6809, 0.544167], [1.0, 4307, 0.736439, 4370, 0.724423, 4569, 0.722162, 4692, 0.724373, 5190, 0.666445, 6027, 0.501658, 6489, 0.510629, 6806, 0.537009], [15.0, 4249, 0.786804, 4370, 0.772612, 4608, 0.765958, 4704, 0.777826, 5178, 0.688189, 6108, 0.495488, 6320, 0.528673, 6790, 0.536223], [17.0, 4249, 0.760152, 4392, 0.748717, 4608, 0.741852, 4708, 0.752673, 5178, 0.681248, 6108, 0.503626, 6331, 0.533375, 6798, 0.546583], [10.0, 4291, 0.774481, 4370, 0.75958, 4646, 0.754338, 4696, 0.761138, 5178, 0.705547, 6027, 0.506657, 6489, 0.520427, 6813, 0.546428], [10.0, 4291, 0.779014, 4370, 0.763569, 4646, 0.758873, 4696, 0.765077, 5178, 0.711456, 6027, 0.51175, 6489, 0.524899, 6817, 0.554545], [12.0, 4249, 0.768835, 4370, 0.753914, 4565, 0.739204, 4700, 0.742487, 5178, 0.6681, 6019, 0.482469, 6316, 0.499849, 6794, 0.508431], [4.0, 4245, 0.727687, 4370, 0.713946, 4604, 0.715886, 4704, 0.723827, 5174, 0.64314, 6023, 0.461703, 6316, 0.494365, 6755, 0.494909], [10.0, 4291, 0.76322, 4370, 0.748589, 4592, 0.743987, 4696, 0.749946, 5178, 0.705359, 6027, 0.503861, 6489, 0.518608, 6817, 0.546065], [12.0, 4326, 0.761143, 4370, 0.747229, 4600, 0.730598, 4700, 0.73555, 5178, 0.652202, 6019, 0.476356, 6312, 0.492859, 6802, 0.50242], [7.0, 4291, 0.764091, 4370, 0.748692, 4600, 0.74797, 4696, 0.75431, 5178, 0.683373, 6023, 0.491274, 6324, 0.505949, 6798, 0.523951], [7.0, 4287, 0.785369, 4370, 0.768557, 4608, 0.766588, 4696, 0.773729, 5178, 0.712835, 6023, 0.502441, 6324, 0.518273, 6798, 0.536194], [16.0, 4318, 0.791626, 4388, 0.781518, 4646, 0.756666, 4712, 0.768906, 5171, 0.727922, 5955, 0.494072, 6489, 0.520662, 6809, 0.551423], [2.0, 4291, 0.799048, 4370, 0.777139, 4646, 0.784524, 4692, 0.79251, 5178, 0.712526, 6030, 0.516265, 6489, 0.527942, 6806, 0.562452], [17.0, 4249, 0.797085, 4392, 0.782184, 4646, 0.777467, 4708, 0.793687, 5171, 0.686822, 6108, 0.499109, 6324, 0.534307, 6794, 0.544286], [11.0, 4287, 0.761239, 4370, 0.745148, 4592, 0.735884, 4696, 0.741885, 5178, 0.698324, 6023, 0.487874, 6489, 0.500494, 6802, 0.525048], [6.0, 4287, 0.763392, 4370, 0.748782, 4584, 0.743001, 4696, 0.747478, 5178, 0.691581, 6023, 0.504511, 6331, 0.515855, 6806, 0.536089]]


# print(len(lst))
def f_y(dif, lst):
    # print("***************" , len(lst))
    # difference_all = 200     # Magic numbers, Don't touch! :)
    difference_all = dif
    norms = [lst[0]]  # 标准对比库  先一第一行数据为标准
    # print("===", norms)
    # count = [1]              # 每种 药材数量
    count = [1]  # 每种 药材或产地 数量
    sampling = len(norms[0])  # 在每种检材上取样次数   len(interval)
    # print("success")

    for i in range(1, len(lst)):  # 设i变量为检材库对比游标   len(lst) == data_size
        # print(len(norms), sum(count))

        # print("检材:", i)
        for j in range(len(norms)):  # 设j变量为标准库对比游标
            # print("标准:", j)

            # print("123", norms)
            sum_dif = 0  # 用误差值累计求和   赋初值为0
            for k in range(2, sampling, 2):  # 步长为2 只计算y轴 差异
                # print("====================", i, j)
                sum_dif += abs(lst[i][k] - norms[j][k])  # 依次计算每种检材与每种标准样的差值 取绝对值
            if sum_dif <= difference_all:  # 如果误差不超过 规定值
                count[j] += 1  # 该种标准下 数量+1
                break
            j += 1
        if j >= len(norms):
            norms.append(lst[i])  # 超过误差 将该检材添加进标准库
            count.append(1)  # 数量库中新增该种样品 数量



    new_count = [i for i in count if i > 10]

    # return count
    return norms

a = f_y(950,table_1)
b = f_y(950,table_2)

print(a)
print(b)

# 950
'''
for k in range(850, 1300, 10):
    no = sorted(f_y(k/100000, table_1), reverse=True)
    no2 = sorted(f_y(k/100000, table_2), reverse=True)
    table = [no[i] + no2[i] for i in range(17)]
    print(table)
'''

'''
for i in range(3000, 5000, 100):
    list_t = sorted(f_y(i/100000, table), reverse=True)
    print(i, list_t[:17], list_t[0] - list_t[16])
    print(i, list_t)
'''
'''
    print(sum(count))
    print(len(lst))

'''




''''''

'''import pandas as pd

l = [6.0, 590, 0.408362, 668, 0.375945, 744, 0.309413, 908, 0.287446, 942, 0.307143, 960, 0.329, 1035, 0.760054, 1055, 0.730104, 1151, 0.469264, 1243, 0.400337, 1311, 0.42824, 1384, 0.536992, 1430, 0.458399, 1569, 0.601398, 1654, 0.796987, 2863, 0.342157, 2926, 0.439352, 3385, 0.76277]  # 检验数据

no = f_y(0.5969, table)           # 标准标准库


def find_origin(data, nom):
    sampling = len(nom[0])
    result = []                                            # 二维列表 用来存放预处理数据
    for r in data.index:
        row = data.loc[r].values[0:]             # 按行读取 附件 数据
        if pd.isnull(row[0]):
            for n in nom:
                sum_dif = 0
                t = []
                for k in range(2, sampling, 2):  # 步长为2
                    sum_dif += abs(row[k] - n[k])
                t.append(sum_dif)
                t.append(nom[0])
            print(t)

    return result


'''

